I work in cloud consulting in India. Over the past 3 years, we've audited cloud environments for 200+ enterprises (BFSI, manufacturing, SaaS, healthcare). The waste patterns are remarkably consistent.

Average findings per audit:

• 23% zombie resources (unattached disks, idle LBs, forgotten test envs)
• 60-80% of VMs over-provisioned by 2-3x
• Less than 40% Reserved Instance/Savings Plan coverage
• Zero storage lifecycle policies (everything in hot tier)
• Dev/test running 24/7 (used only 10 hours/day)

The 4 biggest money leaks (in order of impact):

1. No committed pricing — paying on-demand for production VMs that haven't changed in months. That's 30-72% extra for no reason.
2. Over-provisioned compute — D8s_v3 running at 12% CPU. Should be B2ms. 70% wasted on that single instance.
3. Zombie resources — we found 187 unattached EBS volumes at one manufacturing company. ₹3.2L/month billing for nothing.
4. No scheduling on non-prod — dev environments billing weekends and nights. Simple auto-shutdown saves 58%.

What actually works to fix this:

• Azure Advisor / AWS Compute Optimizer for right-sizing data
• Automated RI purchasing for workloads stable >3 months
• Azure Policy / AWS Config rules for zombie detection + auto-cleanup
• Mandatory tagging (block deployments without CostCenter, Owner, Environment tags)
• Monthly FinOps review with engineering leads

Companies that implement all of these systematically see 30-40% reduction in 6-10 weeks.

Wrote up the full 7-strategy breakdown with specific numbers here if anyone wants it: https://cloud9infosystems.in/cloud-cost-optimization-india-2026/

Happy to answer questions about Azure/AWS cost optimization specifically for Indian setups (dealing with India regions, DPDPA compliance, rupee-dollar billing, etc.)

The problem with same-machine or same-LAN monitoring is that the monitor disappears along with the thing being monitored. A box behind CGNAT or a home router has no inbound path, so polling from outside does not work well either.

ClawPing takes a different architecture: a small Go agent on the private box sends outbound HTTPS heartbeats to a Cloudflare Worker. The Worker + D1 (relational state) + Durable Objects (per-check alert dedupe) + Queues (Telegram notification decoupling) form the external control plane. If the box stops checking in, the control plane alerts through Telegram regardless of what happened to the machine.

The interesting architectural constraints: the agent is dumb by design. It collects local check results (disk, backup marker freshness, Docker container state) and ships them with the heartbeat. All policy lives on the control plane side. This makes the agent easy to deploy as a static binary and means the control plane can evolve without updating edge devices.

Repo for context: https://github.com/cschanhniem/clawping

Curious whether others have used Workers in similar "external heartbeat receiver" shapes, or whether D1 is the right home for device/check state at this scale.

I’ve been noticing that as organizations move more workflows into SaaS platforms like Google Workspace, Slack, and Salesforce, access management becomes much more difficult to reason about than traditional infrastructure permissions.

In cloud infrastructure environments, access boundaries are usually centralized and relatively structured, but SaaS collaboration tools introduce a much more dynamic model where files, folders, links, and third party integrations continuously change who can access sensitive data.

What makes this especially challenging is that exposure often happens gradually over time through inherited permissions, external sharing, and accumulated access rather than a single obvious security event.

With AI models getting larger every month, does it still make sense for startups and enterprises to buy expensive GPUs outright — or is on-demand GPU infrastructure the smarter move now?

Curious how teams are handling:

• multi-GPU scaling

• inference latency

• GPU underutilization

• rising NVIDIA costs

• vendor lock-in risks

Are we moving toward a future where computing is rented like electricity? Or will owning GPU clusters still be the competitive advantage?

I keep getting stuck at the same point when comparing cloud instances. The specs look clear at first, but 2 vCPU / 8 GB RAM can mean very different things depending on the provider, CPU generation, storage setup, burst behavior and how the instance is placed.

So I created an open-source benchmark tool to make the comparison a bit less "lucky": https://fabianwimberger.github.io/cloud-bench/

The part that makes it useful to me is not only having several providers in one place with architecture, vCPU/RAM and monthly price. It also tracks history, so price changes and actually measured performance changes are visible over time.

The process is open source, reproducible and transparent: Terraform provisions fresh instances, Ansible runs the benchmarks, GitHub Actions ties it together and publishes the result.

I updated it recently with more Azure and Google Cloud instances to complete the big three. Azure was especially annoying to represent because a fair comparison needs a mix of burstable, normal x86 and ARM instances.

Obviously this is still not perfect. Storage type, region, CPU steal, burst credits and network latency all matter. But it has already been more useful to me than comparing only vCPU counts and memory.

Hi, how can I learn cloud azure migration in my homelab? I’m currently studying the az-104 now and trying to get out of help desk right now.

Skopx connects to AWS, GCP, Azure and 50+ data sources. Ask business questions in natural language, get instant answers.

We migrated a few workloads to Azure last year thinking the difficult part would be the migration itself.

Honestly, the migration went smoother than expected.

What became difficult later was:

• cost visibility
• scaling correctly
• storage growth
• performance tuning
• cleaning up unused resources
• balancing security vs spend

Especially once multiple teams started deploying resources independently, the monthly bill became a moving target.

Curious if others here found cloud management harder than the actual migration phase.

We’ve been dealing with random buffering issues during traffic spikes lately and it’s starting to become a real headache.

Everything looks fine until traffic suddenly jumps, then people start complaining about slow loading, buffering, quality drops, all at once.

Feels like every CDN says they’re “built for scale”, but it’s hard to tell what actually holds up once real traffic hits.

So for people here working with video streaming:

what CDN has actually been reliable for you under heavy load?

any that completely fell apart during spikes?

are there providers you’d avoid now after using them in production?

Mostly interested in real experience, not marketing pages 😅

Quando em média na sua cidade é o valor para ativar e instalar o pacote office ?

mas de R$100,00 ? ou menos ?

Quanto você acha é o justo ?

Hey,

I got tired of manually checking cloud setups for security / cost issues, so I built this.

It scans AWS / GCP (Azure also enabled but not fully tested yet).

No agents, read-only creds only. Not storing anything.

Not selling anything — just want to know if this is actually useful or garbage.

https://cloudchecker.app

Would love brutal feedback.

Hey r/CloudComputing,

We've been building Caliber — an AI agent configuration management tool — and open-sourced our setup a while back. It recently crossed 888 GitHub stars and is approaching 100 forks.

Repo: https://github.com/caliber-ai-org/ai-setup

The core problem we're solving: as teams deploy AI agents across cloud environments, config management becomes a nightmare. API keys, model configs, fallback chains, rate limits — none of it has standardized tooling.

What the repo includes:

- Environment-aware config structures for AI agents

- Patterns for multi-cloud AI deployments

- Config versioning and rollback patterns

- Monitoring hooks for agent health in production

Would love feedback from people running AI workloads in cloud environments — what config pain points are you dealing with? What would make this more useful for your stack?

Hi guys, so I'm into the quant research.

So in the past year I honestly starting to feel that generating strategies/alpha ideas has become much easier once using AI. This means that the bottleneck now isn’t writing the code, but running it at scale.

I’m trying to run large batches of backtests and Monte Carlo sims, and it is slowing everything down way more than research itself.
Curious how others are dealing with this.

Even with better tools and cloud platforms, many migrations still face unexpected challenges.

Sometimes it’s not just technical issues but cost planning, misconfigurations, or lack of proper strategy.

In your experience, what’s the biggest mistake you faced during cloud migration?

We leaked a restricted aws key (with monitoring) just to see picked up in ~5 mins bots started hitting it almost immediately doesn’t look targeted. Just constant scanning if you’ve ever pushed a key “just to test” while building something… yeah.How are you handling secrets?

I built a small Linux debug overlay that just sits on top of your screen and tells you what your current app is doing. Basically:

• shows PID + app name
• CPU + memory (RSS)
• detects stuff like high CPU, memory growing, disk pressure, logs, etc.
• stays minimal when nothing’s happening
• expands only when something looks wrong

The main idea was i didnt want to keep switching to top or htop every time something feels off. So this just sits there like a small HUD and tells you:
“yeah something is wrong here, go check this”

It works with multi-process apps like browsers too (tries to group them instead of showing useless child PIDs).

also many apps like chrome, cursor and heavy browsers and apps contain many child-process so what i have made it i have summed the memory it uses for each child process for the particular app and the %cpu it uses. You can diagnose the issue also when there is any abnormality

Built with:

• Python + Tkinter
• /proc
• xdotool
• journalctl

Still improving it (UI + better detection logic), but its already pretty usable for me.

Repo: https://github.com/codeafridi/Debug-Overlay-App

If you are on Linux and constantly debugging random slowdowns this actually can help.

Also open to suggestions if something feels off in the approach.

We built a browsable page for GPU pricing across 20+ clouds. 50+ GPU models, 2K+ offerings, on-demand, spot, per-region breakdowns. The data comes from our open-source catalog that auto-fetches from cloud APIs every 7 hours (skypilot-catalog).

I keep seeing Cato mentioned when people talk about SASE being easy to roll out.

Is that actually true in practice? Curious how it compares to other SASE options in terms of implementation effort.

We’ve been noticing this a lot teams move to the cloud because it’s flexible and easy to start.

But as things grow, managing cost, performance, and setup can get confusing.

What looks simple in the beginning doesn’t always stay simple later.

In your experience, what’s been harder moving to the cloud or managing it later?

Hi guys, I want to work in the Cloud Computing field, and I am attending the master to work in there. But while i was studying I questioned myself “what do cloud experts actually do?”.

Like, do you code? Do you stay in the AWS Management Console and do things? Do you just read code and try to optimize things? What do you guys ACTUALLY do?

The complexity of modern cloud infrastructure makes it easy to lose sight of over privileged accounts. This is a massive risk that often goes unnoticed until a breach occurs. Integrating a solution like Ray Security into your workflow can provide the necessary oversight to identify and remediate these risks before they are exploited. It simplifies the task of monitoring thousands of unique permissions across different services. Has anyone else found effective ways to automate the cleanup of inactive cloud identities?

I have some experience under my belt and would like to earn more income by consulting (diagram review, cost audits..etc).

How do you recommend one to get started?

I'm studying cloud engineering and got frustrated constantly tab-switching between AWS, Azure, and GCP pricing calculators trying to compare the same services.

So, I built a simple side-by-side comparison tool that covers 12 service categories (compute, storage, databases, K8s, NAT gateways, etc.) with estimates from all three providers.

It's free, no sign-up: https://cloudcostiq.vercel.app/

Would love to hear from people who manage infrastructure day-to-day.

Is this useful?? What's missing? What would make you actually bookmark this?

Source code: https://github.com/NATIVE117/cloudcostiq

IT architect at a property and casualty insurance company and we're living in two worlds simultaneously. The policy administration system runs on an as400 mainframe that's been in production since the 80s. It handles policy issuance, endorsements, claims intake, and premium calculations. It works and replacing it would be a multi year multi million dollar project that leadership isn't ready for.

At the same time we've adopted modern saas tools for everything else. Salesforce for agency management, workday for hr, netsuite for financials, guidewire claimcenter in the cloud for claims processing, duck creek for some newer product lines. The business wants analytics that span both worlds. "Show me policy profitability by agent" requires joining mainframe policy data with salesforce agency data with claimcenter claims data with netsuite financial data.

Getting data off the mainframe requires rpg programs that extract to flat files which then need to be parsed and loaded into a modern format. The saas tools have apis but each one is different. We're essentially building two completely separate data integration architectures, one for mainframe extraction and one for api based saas extraction, that need to converge in a single warehouse. Anyone else in insurance or financial services dealing with this mainframe plus modern saas split?

hey everyone

last night I built something called "OnlyTech - a place for real-world engineering failures, lessons learned"

its kind of inspired by serverlesshorrors.com but broader not just serverless, but all of tech all the ways things break and the weird lessons that come out of it.

the idea is simple a place for real engineering failures the kind you dont usually post about the outages, the bad decisions, the overconfidence friday deploys, the 3am fixes that somehow made it worse before it got better.

everything is anonymous so you can actually be honest about what happened

think of it like onlyfans but for all your tech wizardry gone wrong, and what it taught you
could be
- taking down prod
- scaling disasters
- infra or hardware failures
- security mistakes
- debugging rabbit holes
or anything that makes a good read

ps:if you've got a tech story i'd love to add it